The anatomic analysis of neuroimaging data sets has traditionally focused on the development of tools to extract or quantify features within the data that are obvious to casual visual inspection. This has resulted in an emphasis on percellating the brain on the basis of large scale macroscopic features. Such features are typically either bounded by abrupt changes in signal intensity (e.g., identification of the thalamus as distinct from the surrounding! white matter. or more generally. segmentation of tissue into gray matter, white matter and CSF), or are defined in terms of their gross morphometric characteristics (e.g., delineation of the boundaries of specific sulci or gyri). The importance and utility of tools to facilitate the analysis of macroscopic anatomic-dc features in neuroimaging data is clear, and the tools for such analyses are so mature that their existence and use is implicit throughout this proposal. The emphasis of this section will be the development of new tools tha t will allow us and our collaborators to push beyond the limits of macroscopic anatomy into the realm of features traditionally associated with microscopic anatomy. Although not necessarily obvious on casual visual inspection of the images, the latest generation of imaging technology has crossed resolution thresholds that allow brain images to be anatomically subdivided on the basis of features that are rooted in the microscopic realm. Tools that are based on a clear understanding of the associated microscopic anatomic fundamentals will allow optimal detection and analysis of these features. assuring that we will be able to take full advantage of each future incremental increase in image quality. Specific Aims 1. Develop methods for enhancing the laminar organization of the cortex so that it can be detected, quantified, and compared in MRI data sets both within and across subjects and during development or aging. Specific Aims Specific Aims 2. Develop general tools that can incorporate contextual information (e.g., texture or local orientation), prior anatomic expectations. or expert neuroanatomic guidance, to generate signals that are optimally sensitive to microscopic anatomic features. Specific Aims 3. S systematically ) explore anatomic resources to identify microscopic features expected to produce characteristic MRI signatures. Specific Aims 4. Anatomically validate the tools developed in this project using unique data sets including post-mortem cryornacrotome data of subjects who underwent antemortem MRI scanning.